SHAP skill: what it does and how to install it
Explains and audits machine-learning predictions with SHAP, selecting explainers, validating feature attributions, and producing local or global visualisations.
Summary generated from the skill's documentation.
Install
$ npx skills add K-Dense-AI/scientific-agent-skills --skill shapRun it in a terminal. If your agent is already running, start a new session so it picks the skill up.
About this skill
What it does. Guides SHAP analysis from defining the model output and background population through explainer and masker selection, modern shap.Explanation computation, additivity checks, and local or global plots. Covers tabular, multi-output, text, image, cohort and model-agnostic workflows, with troubleshooting and reporting guidance.
When to use it. Use it to audit how a fixed model maps inputs to outputs. It does not replace predictive validation or establish causality, fairness, recourse, or scientific mechanism.
History
Repo stars
47.3kAbout +18.6k since 9 Jul 2026
Before 1 Oct 2026 the curve is estimated from public event data.
Stars are counted for the whole repository, which holds 50 skills.
Show as a table
| Date | Repo stars |
|---|---|
| 1 Oct 2026 | 47,271 |
| 24 Sept 2026 (estimated) | 46,976 |
| 17 Sept 2026 (estimated) | 46,257 |
| 10 Sept 2026 (estimated) | 41,836 |
| 3 Sept 2026 (estimated) | 30,545 |
| 27 Aug 2026 (estimated) | 29,347 |
| 20 Aug 2026 (estimated) | 29,160 |
| 13 Aug 2026 (estimated) | 29,027 |
| 6 Aug 2026 (estimated) | 28,947 |
| 30 Jul 2026 (estimated) | 28,894 |
| 23 Jul 2026 (estimated) | 28,894 |
| 16 Jul 2026 (estimated) | 28,841 |
| 9 Jul 2026 (estimated) | 28,708 |
Installs
1.8k
Tracking since . A chart appears once there are 7 days of data.
Installs via skills.sh
Similar skills
- scikit-learnMachine learning in Python with scikit-learn: classification, regression, clustering, model evaluation and tuning.
- Exploratory Data AnalysisRuns exploratory analysis on scientific data files: profiles, missing-data audits and outlier checks.
- Scientific VisualizationCreates and audits publication-ready scientific figures with Matplotlib, Seaborn or Plotly.